Deep network path analysis for identifying network segments affecting application performance

ABSTRACT

In one embodiment, a network analysis process initiates network path analysis for a transaction application operating over a logical transaction path having a first segment from a first set of transaction servers to a load balancer and a second segment then to a second set of transaction servers. The network path analysis, when for the second segment, comprises: selecting a receiving transaction server of the second set of transaction servers; identifying a TCP session associated with the transaction application already in progress to the receiving transaction server; initiating a TCP traceroute using ACK packets, whose signature matches the in-progress TCP session, from the receiving transaction server to the load balancer; and determining, in reverse, a network path of layer-3 segments and associated network metrics between the receiving transaction server and the load balancer. Specific layer-3 segments of the network path causing performance degradation of the transaction application are then identifiable.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to deep network path analysis for identifying networksegments affecting application performance.

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses. Due to theaccompanying complexity of the infrastructure supporting the webservices, it is becoming increasingly difficult to maintain the highestlevel of service performance and user experience to keep up with theincrease in web services. For example, it can be challenging to piecetogether monitoring and logging data across disparate systems, tools,and layers in a network architecture. Moreover, even when data can beobtained, it is difficult to directly connect the chain of events andcause and effect.

In one particular example, it can be difficult to determine the reasonfor an application to be experiencing an issue. For instance,determining whether it is the application that is causing the issue asopposed to the network through which the application is communicating isonly the first step to troubleshooting problems. Even after establishingthe network as a contributor to the problems, customers want todetermine the specific network segments that are contributing to theapplication's performance issues. Currently, the tools available in themarket involve a lot of manual stitching together of data between theapplication and the network, thus leading to inaccuracies and increasesin the mean-time to determine the root cause of the issue.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for an application-aware intrusiondetection system;

FIG. 5 illustrates an example computing system implementing thedisclosed technology;

FIG. 6 illustrates an example graph of application transactions chartedagainst application latency;

FIG. 7 illustrates an example graphical user interface (GUI) showing alogical transaction path and associated information;

FIG. 8 illustrates an example GUI showing the logical transaction pathand associated information in an example network underlay view;

FIG. 9 illustrates an example of a network path corresponding to thelogical transaction path;

FIG. 10 illustrates an example of traceroute processing foridentification of network segments;

FIG. 11 illustrates another example GUI showing the logical transactionpath and associated information in an example network underlay view;

FIG. 12 illustrates another example of traceroute processing foridentification of network segments, in reverse;

FIG. 13 illustrates an example of a GUI representing an illustrativeoutput of deep network path analysis;

FIG. 14 illustrates an example procedure for deep network path analysisfor identifying network segments affecting application performance inaccordance with one or more embodiments described herein;

FIG. 15A illustrates an example sub-procedure for deep network pathanalysis in accordance with one or more embodiments described herein,particularly where the network path analysis is for a segment of thelogical transaction path from a load balancer to second (right-side) setof servers; and

FIG. 15B illustrates another example sub-procedure for deep network pathanalysis in accordance with one or more embodiments described herein,particularly where the network path analysis is for a segment of thelogical transaction path from a first (left-side) set of servers to theload balancer.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a networkanalysis process determines a trigger to initiate network path analysisfor a transaction application operating over a logical transaction pathhaving a first segment from a first set of transaction servers to a loadbalancer and a second segment from the load balancer to a second set oftransaction servers. In response to the trigger, the network pathanalysis is initiated, where, in one embodiment, the network pathanalysis is for the second segment of the logical transaction path.Accordingly, a receiving transaction server of the second set oftransaction servers is selected, and a transmission control protocol(TCP) session associated with the transaction application already inprogress to the receiving transaction server is identified. A TCPtraceroute using acknowledgment (ACK) packets, whose signature matchesthe TCP session already in progress, may then be initiated from thereceiving transaction server to the load balancer in order to determine,in reverse, a network path of layer-3 segments between the receivingtransaction server and the load balancer and one or more network metricsassociated with each layer-3 segment of the network path based on theTCP traceroute. As such, the network analysis process may identify oneor more specific layer-3 segments of the network path causingperformance degradation of the transaction application based on thenetwork path analysis.

According to one or more embodiments of the disclosure, the network pathanalysis is for the first segment of the logical transaction path. Assuch, the network path analysis selects an originating transactionserver of the first set of transaction servers, and initiates a TCPtraceroute using synchronize (SYN) packets associated with thetransaction application from the originating transaction server to theload balancer. The network path analysis may then determine a networkpath of layer-3 segments between the originating transaction server andthe load balancer and one or more network metrics associated with eachlayer-3 segment of the network path of layer-3 segments between theoriginating transaction server and the load balancer based on the TCPtraceroute. From this, the network analysis process may then identifyone or more specific layer-3 segments of the network path of layer-3segments between the originating transaction server and the loadbalancer causing performance degradation of the transaction applicationbased on the network path analysis.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. The Internet is an exampleof a WAN that connects disparate networks throughout the world,providing global communication between nodes on various networks. Othertypes of networks, such as field area networks (FANs), neighborhood areanetworks (NANs), personal area networks (PANs), enterprise networks,etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discreteframes or packets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other. Computer networks may be furtherinterconnected by an intermediate network node, such as a router, toextend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or power-line communication networks. That is, in addition toone or more sensors, each sensor device (node) in a sensor network maygenerally be equipped with a radio transceiver or other communicationport, a microcontroller, and an energy source, such as a battery.Generally, size and cost constraints on smart object nodes (e.g.,sensors) result in corresponding constraints on resources such asenergy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations. Servers 152-154 may include,in various embodiments, any number of suitable servers or othercloud-based resources. As would be appreciated, network 100 may includeany number of local networks, data centers, cloud environments,devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc. Furthermore, in various embodiments, network 100 mayinclude one or more mesh networks, such as an Internet of Thingsnetwork. Loosely, the term “Internet of Things” or “IoT” refers touniquely identifiable objects (things) and their virtual representationsin a network-based architecture. In particular, the next frontier in theevolution of the Internet is the ability to connect more than justcomputers and communications devices, but rather the ability to connect“objects” in general, such as lights, appliances, vehicles, heating,ventilating, and air-conditioning (HVAC), windows and window shades andblinds, doors, locks, etc. The “Internet of Things” thus generallyrefers to the interconnection of objects (e.g., smart objects), such assensors and actuators, over a computer network (e.g., via IP), which maybe the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, areoften on what is referred to as Low-Power and Lossy Networks (LLNs),which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device(e.g., apparatus) 200 that may be used with one or more embodimentsdescribed herein, e.g., as any of the devices shown in FIGS. 1A-1Babove, and particularly as specific devices as described further below.The device may comprise one or more network interfaces 210 (e.g., wired,wireless, etc.), at least one processor 220, and a memory 240interconnected by a system bus 250, as well as a power supply 260 (e.g.,battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork 100, e.g., providing a data connection between device 200 andthe data network, such as the Internet. The network interfaces may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. For example, interfaces 210 may include wiredtransceivers, wireless transceivers, cellular transceivers, or the like,each to allow device 200 to communicate information to and from a remotecomputing device or server over an appropriate network. The same networkinterfaces 210 also allow communities of multiple devices 200 tointerconnect among themselves, either peer-to-peer, or up and down ahierarchy. Note, further, that the nodes may have two different types ofnetwork connections 210, e.g., wireless and wired/physical connections,and that the view herein is merely for illustration. Also, while thenetwork interface 210 is shown separately from power supply 260, fordevices using powerline communication (PLC) or Power over Ethernet(PoE), the network interface 210 may communicate through the powersupply 260, or may be an integral component of the power supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise one or more functional processes 246, and on certain devices,an illustrative “deep path analysis” process 248, as described herein.Notably, functional processes 246, when executed by processor(s) 220,cause each particular device 200 to perform the various functionscorresponding to the particular device's purpose and generalconfiguration. For example, a router would be configured to operate as arouter, a server would be configured to operate as a server, an accesspoint (or gateway) would be configured to operate as an access point (orgateway), a client device would be configured to operate as a clientdevice, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

——Application Intelligence Platform——

The embodiments herein relate to an application intelligence platformfor application performance management. In one aspect, as discussed withrespect to FIGS. 3-5 below, performance within a networking environmentmay be monitored, specifically by monitoring applications and entities(e.g., transactions, tiers, nodes, and machines) in the networkingenvironment using agents installed at individual machines at theentities. For example, each node can include one or more machines thatperform part of the applications. The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata may include performance data (e.g., metrics, metadata, etc.) andtopology data (e.g., indicating relationship information). Theagent-collected data may then be provided to one or more servers orcontrollers to analyze the data.

FIG. 3 is a block diagram of an example application intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The application intelligence platform is a system that monitorsand collects metrics of performance data for an application environmentbeing monitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310 and one or moreservers/controllers 320. Note that while FIG. 3 shows four agents (e.g.,Agent 1 through Agent 4) communicatively linked to a single controller,the total number of agents and controllers can vary based on a number offactors including the number of applications monitored, how distributedthe application environment is, the level of monitoring desired, thelevel of user experience desired, and so on.

The controller 320 is the central processing and administration serverfor the application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions (described below) distributed over application servers.Specifically, the controller 320 can receive runtime data from agents310 (and/or other coordinator devices), associate portions of businesstransaction data, communicate with agents to configure collection ofruntime data, and provide performance data and reporting through theinterface 330. The interface 330 may be viewed as a web-based interfaceviewable by a client device 340. In some implementations, a clientdevice 340 can directly communicate with controller 320 to view aninterface for monitoring data. The controller 320 can include avisualization system 350 for displaying the reports and dashboardsrelated to the disclosed technology. In some implementations, thevisualization system 350 can be implemented in a separate machine (e.g.,a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance 320 may be hosted remotely by a provider of theapplication intelligence platform 300. In an illustrative on-premise(On-Prem) implementation, a controller instance 320 may be installedlocally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310 can be implemented as different types of agents with specificmonitoring duties. For example, application agents may be installed oneach server that hosts applications to be monitored. Instrumenting anagent adds an application agent into the runtime process of theapplication.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Database agents query the monitoreddatabases in order to collect metrics and pass those metrics along fordisplay in a metric browser (e.g., for database monitoring and analysiswithin databases pages of the controller's UI 330). Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs(e.g., standalone Java programs) that collect hardware-relatedperformance statistics from the servers (or other suitable devices) inthe monitored environment. The standalone machine agents can be deployedon machines that host application servers, database servers, messagingservers, Web servers, etc. A standalone machine agent has an extensiblearchitecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents andmobile agents to provide performance information from the point of viewof the client, such as a web browser or a mobile native application.Through EUM, web use, mobile use, or combinations thereof (e.g., by realusers or synthetic agents) can be monitored based on the monitoringneeds. Notably, browser agents (e.g., agents 310) can include Reportersthat report monitored data to the controller.

Browser agents and mobile agents are generally unlike other monitoringthrough application agents, database agents, and standalone machineagents that are on the server. In particular, browser agents maygenerally be embodied as small files using web-based technologies, suchas JavaScript agents injected into each instrumented web page (e.g., asclose to the top as possible) as the web page is served, and areconfigured to collect data. Once the web page has completed loading, thecollected data may be bundled into a beacon and sent to an EUMprocess/cloud for processing and made ready for retrieval by thecontroller. Browser real user monitoring (Browser RUM) provides insightsinto the performance of a web application from the point of view of areal or synthetic end user. For example, Browser RUM can determine howspecific Ajax or iframe calls are slowing down page load time and howserver performance impact end user experience in aggregate or inindividual cases.

A mobile agent, on the other hand, may be a small piece of highlyperformant code that gets added to the source of the mobile application.Mobile RUM provides information on the native mobile application (e.g.,iOS or Android applications) as the end users actually use the mobileapplication. Mobile RUM provides visibility into the functioning of themobile application itself and the mobile application's interaction withthe network used and any server-side applications with which the mobileapplication communicates.

Application Intelligence Monitoring: The disclosed technology canprovide application intelligence data by monitoring an applicationenvironment that includes various services such as web applicationsserved from an application server (e.g., Java virtual machine (JVM),Internet Information Services (IIS), Hypertext Preprocessor (PHP) Webserver, etc.), databases or other data stores, and remote services suchas message queues and caches. The services in the applicationenvironment can interact in various ways to provide a set of cohesiveuser interactions with the application, such as a set of user servicesapplicable to end user customers.

Application Intelligence Modeling: Entities in the applicationenvironment (such as the JBoss service, MQSeries modules, and databases)and the services provided by the entities (such as a login transaction,service or product search, or purchase transaction) may be mapped to anapplication intelligence model. In the application intelligence model, abusiness transaction represents a particular service provided by themonitored environment. For example, in an e-commerce application,particular real-world services can include a user logging in, searchingfor items, or adding items to the cart. In a content portal, particularreal-world services can include user requests for content such assports, business, or entertainment news. In a stock trading application,particular real-world services can include operations such as receivinga stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of theparticular service provided by the monitored environment provides a viewon performance data in the context of the various tiers that participatein processing a particular request. A business transaction, which mayeach be identified by a unique business transaction identification (ID),represents the end-to-end processing path used to fulfill a servicerequest in the monitored environment (e.g., adding items to a shoppingcart, storing information in a database, purchasing an item online,etc.). Thus, a business transaction is a type of user-initiated actionin the monitored environment defined by an entry point and a processingpath across application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request(e.g., a socket call, illustratively associated with the TCP layer). Abusiness transaction can be created by detecting incoming requests at anentry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment (e.g., associating the business transaction with a 4-tupleof a source IP address, source port, destination IP address, anddestination port). A flow map can be generated for a businesstransaction that shows the touch points for the business transaction inthe application environment. In one embodiment, a specific tag may beadded to packets by application specific agents for identifying businesstransactions (e.g., a custom header field attached to an HTTP payload byan application agent, or by a network agent when an application makes aremote socket call), such that packets can be examined by network agentsto identify the business transaction identifier (ID) (e.g., a GloballyUnique Identifier (GUID) or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or Common Language Runtime (CLR) onwhich a monitoring Agent is installed. Each node identifies itself inthe application intelligence model. The Agent installed at the node isconfigured to specify the name of the node, tier, and businessapplication under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which,along with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier. Similarly, a machine agent cannot belong to more than onetier. However, more than one machine agent can be installed on amachine.

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for system by detecting slow, very slow, andstalled transactions. Configurable thresholds provide a flexible way toassociate the right business context with a slow request to isolate theroot cause.

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected. Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline”. The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. In some implementations, this healthrule can define a warning condition as the combination of an averageresponse time greater than the default baseline by 2 standard deviationsand a load greater than 100 calls per minute. In some implementations,the health rule that tests whether the business transaction error rateis much higher than normal can define a critical condition as thecombination of an error rate greater than the default baseline by 3standard deviations and an error rate greater than 10 errors per minuteand a load greater than 50 calls per minute. In some implementations,this health rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute. These are non-exhaustive and non-limitingexamples of health rules and other health rules can be defined asdesired by the user.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) API that returns either the JavaScript ObjectNotation (JSON) or the eXtensible Markup Language (XML) format. Also,the REST API can be used to query and manipulate the applicationenvironment.

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

FIG. 4 illustrates an example application intelligence platform (system)400 for performing one or more aspects of the techniques herein. Thesystem 400 in FIG. 4 includes client device 405 and 492, mobile device415, network 420, network server 425, application servers 430, 440, 450,and 460, asynchronous network machine 470, data stores 480 and 485,controller 490, and data collection server 495. The controller 490 caninclude visualization system 496 for providing displaying of the reportgenerated for performing the field name recommendations for fieldextraction as disclosed in the present disclosure. In someimplementations, the visualization system 496 can be implemented in aseparate machine (e.g., a server) different from the one hosting thecontroller 490.

Client device 405 may include network browser 410 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection 460, controller 490, or another device.Agent 412 may perform other operations related to monitoring a requestor a network at client 405 as discussed herein including reportgenerating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or other portable device. Both client device 405 and mobiledevice 415 may include hardware and/or software configured to access aweb service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may repot data to datacollection server 460 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 3).Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.,“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4.

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorcode running in a virtual machine 432 (or other program language, suchas a PHP, .Net, or C program), machine resources, network layer data,and communicate with byte instrumented code on application server 430and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a Java agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier or nodes or other entity. Anode may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agent may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from one or morenetwork interfaces (e.g., so that data generated/received by all theapplications using sockets can be intercepted). The network agent mayreceive flow data from a language agent that is associated withapplications to be monitored. For flows in the flow group data thatmatch flow data provided by the language agent, the network agent rollsup the flow data to determine metrics such as TCP throughput, TCP loss,latency, and bandwidth. The network agent may then report the metrics,flow group data, and call chain data to a controller. The network agentmay also make system calls at an application server to determine systeminformation, such as for example a host status check, a network statuscheck, socket status, and other information.

A machine agent may reside on the host and collect information regardingthe machine which implements the host. A machine agent may collect andgenerate metrics from information such as processor usage, memory usage,and other hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or 415associated with a user or another source). The request identifier may besent to client 405 or mobile device 415, whichever device sent therequest. In embodiments, the request identifier may be created when adata is collected and analyzed for a particular business transaction.

Each of application servers 440, 450, and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452, and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454, and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452, and462 may communicate with each other as part of performing a distributedtransaction. Each application may call any application or method ofanother virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 450. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 460. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444, and 454 (also referred to hereinas “application monitoring agents”). Controller 490 may associateportions of business transaction data, communicate with agents toconfigure collection of data, and provide performance data and reportingthrough an interface. The interface may be viewed as a web-basedinterface viewable by client device 492, which may be a mobile device,client device, or any other platform for viewing an interface providedby controller 490. In some embodiments, a client device 492 may directlycommunicate with controller 490 to view an interface for monitoringdata.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, work station or othercomputing device. Client computer 492 may communicate with controller390 to create and view a custom interface. In some embodiments,controller 490 provides an interface for creating and viewing the custominterface as a content page, e.g., a web page, which may be provided toand rendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology, which is a specific implementation of device 200 ofFIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts ofthe likes of clients 405, 492, network server 425, servers 430, 440,450, 460, a synchronous network machine 470, and controller 490 of FIG.4. (Note that the specifically configured system 500 of FIG. 5 and thecustomized device 200 of FIG. 2 are not meant to be mutually exclusive,and the techniques herein may be performed by any suitably configuredcomputing device.)

The computing system 500 of FIG. 5 includes one or more processors 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor 510. Main memory 510 can store the executablecode when in operation. The system 500 of FIG. 5 further includes a massstorage device 530, portable storage medium drive(s) 540, output devices550, user input devices 560, a graphics display 570, and peripheraldevices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable or remote storagedevice 540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present invention for purposes of loading thatsoftware into main memory 520.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a compact disk, digital video disk,magnetic disk, flash storage, etc. to input and output data and code toand from the computer system 500 of FIG. 5. The system software forimplementing embodiments of the present invention may be stored on sucha portable medium and input to the computer system 500 via the portablestorage device 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, hand held computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

——Identifying Network Segments Affecting Application Performance——

As noted above, it can be difficult to determine the actual reason foran application to be experiencing an issue, particularly whether it isthe application or the underlying network that is causing the issue.Even where it can be established that the network is a contributor tothe problems, it is still important to determine the specific networksegments that are contributing to the application's performance issues.That is, the ability to identify where and which network elements (alonga logical business transaction path) are causing either consistent orintermittent performance degradations (e.g., latency) is particularlyuseful for administrators and mitigation processes, and has yet to beadequately addressed by previous technologies.

For example, one particular impact of such performance degradations isthe creation of business transaction “outliers”. For instance, theillustrative application intelligence platform 400 above can trackindividual business transactions within an application (applicationtransactions), and the relative performance of application transactionsin the aggregate, and can thus identify the “long tail” of applicationtransaction latency, representing application transaction outliers thathave significant negative impact on the end user's experience. FIG. 6illustrates an example graph 600 of a number (#) of applicationtransactions 610 charted against application latency 620 (e.g., inmilliseconds (msec)), showing a collection of outliers 630 (e.g., thatgenerally surpass some threshold of tolerance).

Since the network can have a significant impact on business transactionperformance (e.g., the long tail of latency, e.g., outliers 630 as shownin FIG. 6 above), such as due to its latency, packet drops, delay,jitter, etc., it is important to identify where and which networkelement is causing such performance degradations along the businesstransaction's path.

The techniques herein, therefore, provide for different techniques ofdeep path analysis that allow identifying the specific layer-3 segmentsthat are causing the degradation to the business transaction. Inparticular, if the degradation is happening on the left side of the loadbalancer, deep path analysis initiates a TCP traceroute usingsynchronize (SYN) packets from one of the nodes of the left-side servers(e.g., an ecommerce or “ECom” Tier) to return the full path to the loadbalancer and to identify the specific layer-3 segments that are causingthe degradation to the business transaction. Similarly, if thedegradation is happening on the right side of the load balancer, deeppath analysis is initiated from one of the nodes of the right-sideservers (e.g., an Order Tier). Here, the business-transaction-aware deeppath analysis picks a specific business transaction application port andthen identifies a TCP session already in progress, initiating a TCPtraceroute using acknowledgment (ACK) packets that then match thesession in progress in order to return the full path reverse to the loadbalancer and to identify the specific layer-3 segments that are causingthe degradation to the business transaction, accordingly.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative deep path analysis process 248, which may include computerexecutable instructions executed by the processor 220 to performfunctions relating to the techniques described herein, e.g., inconjunction with corresponding processes of other devices in thecomputer network as described herein (e.g., on network agents,controllers, computing devices, servers, etc.).

Operationally, the techniques herein first identify the specificbusiness transaction segment (application transaction segment) that isexperiencing performance degradation (e.g., latency degradation, amongother metrics indicative of performance degradation). As shown in FIG.7, for example, an illustrative graphical user interface (GUI) shows alogical transaction path 700 (e.g., an application flow map) andassociated information regarding an application flow. For example, oneor more ecommerce (ECom) servers 710 (of an ECom tier) may participatein application transactions with one or more order servers 720 (of anOrder tier) and one or more inventory servers 730 (of an Inventory tier)over application transaction “segments” 705. Also, the Order tier mayfurther participate with one or more payment servers 740 (of a Paymenttier). Various databases may also be associated with each set ofservers/tiers, and the view shown herein is simplified for sake ofdiscussion.

Information displayed on the GUI of the logical transaction path 700 maycomprise various application metrics, such as number or rate of calls,average transaction latency, underlying protocol (e.g., HTTP, WebService, etc.), number of nodes/devices at each tier (e.g., number ofservers being monitored that are associated with the application flow),and so on.

Assume for the sake of discussion that the specific applicationtransaction (business transaction) segment 705 that is experiencingperformance (e.g., latency) degradation is detected as segment “705-x”.In one embodiment, the application intelligence platform dynamicallydetermines the performance degradation (e.g., based on policies,thresholds, baseline comparisons, etc.), while in an additional oralternative embodiment, a user identifies the specific segment that isexperiencing latency degradation though manual interpretation of theinformation.

FIG. 8 represents a network underlay view 800 (e.g., network flow map),which may be illustratively selected by the user within a GUI, to showthe specific network segments 805. In particular, one or more loadbalancers, such as load balancer 810 between the ECom servers 710 andOrder servers 720, as well as load balancer 820 between the ECom server710 and Inventory servers 730, may now be shown to demonstrate theseparate of network segments to either side of the load balancers. Forexample, in traditional “east-west” traffic (i.e., traffic betweenservers, as opposed to “north-south” traffic between servers andclients), multiple similarly capable servers may be located on each sideof the load balancer (e.g., one-to-many, many-to-one, and many-to-many)in order to distribute traffic demands among the sets of servers. Asused herein, “left-side” servers are those originating the traffic tothe load balancers (and similarly, over “left-side” segments), while“right-side” servers are those receiving the traffic from the loadbalancers (and similarly, over “right-side” segments).

From the network underlay view 800, it can be specifically seen thatfirst and second network segments (left-side and right-side segments)exist between the ECom servers 710 and Order servers 720, via a loadbalancer 810. As such, the techniques herein allow forverifying/determining whether the degradation is due to a network issueon a specific side of the load balancer 810.

If the performance degradation is happening on the left-side of the loadbalancer, shown as selected segment 805-x, or if the platform/usersimply wants to determine whether the degradation is happening on thatside, then deep path analysis may be initiated (e.g., dynamically or atthe request of a user) from one of the nodes/servers 710 of the EComtier. (In certain embodiments, users may also have a choice of runningtests on demand, such as to monitor the general health of a network,e.g., without any issues (such as for baselining or for other efficiencyconfigurations)).

Notably, the network segments 805 may be more detailed than applicationsegments 705, but still do not fully show the complete layer-3 pathbetween application transaction devices. For instance, as shown in FIG.9, a layer-3 network path 900 corresponding to the logical transactionpaths above (700 or 800) is shown, where layer-3 (or “L3”) segments 905are located between each layer-3 device. As shown, source end point 910(e.g., a particular port of an ECom server “ECom Node1” 710)communicates over a communication channel made up of layer-3 segments905, connecting one or more L3 switches 920 and a load balancer (LB) 930(e.g., specific load balancer 810), to a destination end point 940(e.g., Order server “Order Node1” 720). Note further that certainportions of the network 900 may comprise equal-cost multi-path (ECMP)segments, shown as a side-bar illustration 950, as will be appreciatedby those skilled in the art.

According to the techniques herein, the initiated deep path analysis forthe left-side segment (from originating servers 910/710 to load balancer930/810) may select a specific application transaction port, andinitiates a TCP traceroute from that selected port/server. For instance,as shown in FIG. 10, illustrating an example of traceroute processing1000 for identification of network (layer-3) segments, synchronize (SYN)packets 1010 may be used to provide a detailed hop-by-hop view of thelayer-3 network, as well as associated network metrics (e.g., latency,delay, packet drops, jitter, etc.).

For example, traceroute processing may generally use iterative packets(SYN packets 1010 in the current embodiment) sent between a source node(server 910) and destination node (load balancer 930) in a manner thattries to figure out all elements in between the end points along thecommunication channel to determine the intermediate links and nodes, andto determine associated network metrics. The iterative tracerouteprocess, understood by those skilled in the art, increments time-to-live(TTL) values within the packets 1010, so that each intermediate nodealong the way will, at one point, receive an expiring TTL, and will getits chance to return a TTL=0 error (expiry error) in a return message1020. (Note that in certain embodiments, an encapsulation field may beconfigured within the traceroute SYN packets 1010 to carry informationsuch as node IDs and network metrics forward to another participatingagent process.)

Regarding the captured information, each layer-3 segment 905 may beassociated with some computable network metric. For instance, a TTLexpiry error (e.g., a TCP/ICMP error) may return various metrics, suchas latency to the expiring node, a node ID, and so on. Also, metricssuch as total round-trip-time (from sending the packet 1010 to receivingan error reply 1020) may also be captured by the deep path analysisprocess (e.g., an agent process) on the source node, where latency toeach particular hop may not be specifically shared. As an example,assume a first message has a TTL=1, and reaches the first node beforebeing rejected (e.g., ICMP drop or ICMP reject), returning an error tothe source. The information obtained from this first packet is thatthere is a device (intermediate node) with a given node ID on thecommunication channel at the first hop. We may not, however, know thelatency from this message (that is, we know the latency based on timingof ICMP response and original TCP message when it was sent, thus beingable to extrapolate how long it takes the TCP message to be sent andacknowledged—i.e., round-trip-time). For each subsequent hop (TTL=2, 3,etc.), therefore, the techniques herein can determine one or morenetwork metrics (latency, round-trip time, etc.) associated with eachlayer-3 segment of the network path based on subtracting previous totalnetwork metrics of a previous iteration of the TCP traceroute from acurrent total network metric associated with a current iteration of theTCP traceroute. That is, by subtracting metrics of iteration “TTL=n”from iteration “TTL=n+1”, the techniques herein can specificallydetermine the metrics associated with the final link in the TTL=n+1chain.

Note that this TCP traceroute processing is also fully ECMP multipathaware (again, illustrated in side-bar 1050), where various instances ofthe TCP traceroute may traverse certain links at certain times, andother links at other times (e.g., based on standard ECMP techniques,such as hashing, equal distribution, etc.). For example, a number oftraceroute traces may be performed, either a number of tracesincrementally moving from TTL=1 to TTL=n reaching the load balancer, orelse a number of TTL=1 SYN packets, a number of TTL=2 SYN packets, andso on, each attempting to achieve a different view of the ECMPmulti-paths.

The deep path analysis on the left-side network segment thus returns thefull layer-3 path from the originating server 710 to the load balancer810, and identifies the specific layer-3 segments 905 and their metricssuch that the cause the application's performance degradation can bedetermined, accordingly.

Similarly, according to the techniques herein, if the performancedegradation is happening (or potentially occurring) on the right-sidesegment (between the load balancer 810 and the receiving servers 720),such as shown in FIG. 11 (selected segment 1105-x from within view 800),the deep path analysis may be initiated from one of the receivingnodes/servers (e.g., of the Order Tier).

In particular, with reference to traceroute processing 1200 of FIG. 12,the application transaction (business transaction) aware deep pathanalysis may select a specific application port on a selected receivingserver 720, and identifies a TCP session 1205 already in progress forthe application. The techniques herein may then initiate a TCPtraceroute using an acknowledgment (ACK) packet 1210 whose signaturematches those of the TCP session in progress. (Again, this TCPtraceroute 1200 is also fully ECMP multipath aware, as shown in side-bar1250.) Similar to the incremental traceroute technique 1000 in FIG. 10above, the TCP traceroute 1200 in FIG. 12 uses ACK packets 1210 to tracethe layer-3 path in reverse from the receiving server 720 toward theload balancer 810, with TTL values from 1 to ‘n’ until reaching the loadbalancer (with returned TTL expiry error messages 1220). (Note againthat these ACK packets may include an encapsulated field for carryingadditional information.) The deep path analysis in this direction,therefore, will return the full path reverse to the load balancer andidentify the specific layer-3 segments 905 and associated networkmetrics that could be determined to be the cause of the performancedegradation of the application transactions.

FIG. 13 illustrates an example of a GUI 1300 representing anillustrative output available from deep network path analysis.

Notably, between two application Tier nodes, there may be no way toaccomplish a traditional traceroute from end-to-end. That is, loadbalancers work on URLs, so there is often no way to send TCP requests tocertain destinations. (Unless, that is, you have an agent on the loadbalancer that can participate and continue the analysis process in theproper direction.) In this instance, collaboration between multipleagents may be necessary to form a complete the view of the network.Therefore, by stitching the layer-3 segments of the first/left andsecond/right network segments portions together (e.g., on a centralizedcontroller), total visibility to both sides of the load balancer can beachieved herein.

Notably, the techniques herein may be specifically configured to provideanalysis based on an application context. That is, in conjunction withthe illustrative application intelligence platform described above,various correlations to applications (e.g., including businesstransactions, specifically) may be made to the network segments andassociated network metrics. For example, by mimicking the applicationcontext in the transmitted packets 1010/1210, or more particularlyincluding the context of a business transaction (e.g., a GUID within thepackets), then the network metrics may be specially related to theapplication/transactions. This is particularly useful in cases whereapplications or transactions may be handled differently, such as throughvarious proxies, firewalls, etc.

According to one or more embodiments herein, various mitigation(remediation) actions may be taken in response to isolating particularnetwork segments affecting application performance, such as remediatingagainst those issues (e.g., delay, packet loss, transactionmisdirection, etc.) by reconfiguring the applications, the networks, orother attributes of the communication that could alleviate the issueswith the application performance. Other actions, such as alarms,reports, displays (e.g., on a GUI), etc., may also be performed.

FIG. 14 illustrates an example procedure for deep network path analysisfor identifying network segments affecting application performance inaccordance with one or more embodiments described herein. For example,one or more non-generic, specifically configured devices may performprocedure 1400 by executing stored instructions (e.g., deep pathanalysis process 248, aka a “network analysis process”). The proceduremay start at step 1405, and continues to step 1410, where, as describedin greater detail above, a trigger is determined to initiate networkpath analysis for a transaction application operating over a logicaltransaction path 600 having a first segment 805 (805-x) from afirst/originating set of transaction servers 710 (e.g., left-sideservers, such as ECom Tier servers) to a load balancer 810 and a secondsegment 805 (1105-x) from the load balancer to a second/receiving set oftransaction servers 720 (e.g., right-side servers, such as Order Tierservers). For example, as noted above, the trigger may be a detection ofperformance degradation of the transaction application (e.g., surpassinga threshold tolerance of application-based latency or other parameters),or else may specifically be a user request (e.g., through a GUI orotherwise).

In step 1415, in response to the trigger, the network path analysis maybe initiated. In step 1420, in particular, the process determineswhether the performance degradation is based on either the first networksegment or the second network segment (e.g., dynamically or else byreceiving an indication within the user request). If the performancedegradation is based on the second network segment (e.g., between theload balancer and the second (right-side) set of servers), then theprocedure continues in step 1425 to sub-procedure 1500A of FIG. 15Abelow to perform network path analysis to determine correspondinglayer-3 segments and associated network metrics (e.g., latency, packetdrops, delay, jitter, etc.) on the second network segment. Otherwise, ifthe performance degradation is based on the first network segment (e.g.,between the first (left-side) set of servers and the load balancer),then the procedure continues in step 1430 to sub-procedure 1500B of FIG.15B below to perform network path analysis to also determinecorresponding layer-3 segments and associated network metrics on thefirst network segment.

Procedure 1400 then continues to step 1435, upon completion of theappropriate sub-procedure above, to identify one or more specificlayer-3 segments of the analyzed network path that are causingperformance degradation of the transaction application based on thenetwork path analysis (e.g., segments and their network metrics),accordingly.

Optionally, in step 1440, the performance degradation of the transactionapplication may be mitigated based on the one or more specific layer-3segments causing the performance degradation, as described above. Also,in optional step 1445, the one or more specific layer-3 segments causingthe performance degradation and the one or more network metricsassociated with the one or more specific layer-3 segments causing theperformance degradation may be displayed on a GUI.

The illustrative procedure 1400 may then end in step 1450.

FIG. 15A, as noted, illustrates an example sub-procedure for deepnetwork path analysis in accordance with one or more embodimentsdescribed herein, particularly where the network path analysis is forthe second segment of the logical transaction path (e.g., from the loadbalancer to the second (right-side) set of servers). The sub-procedure1500A may start at step 1505 (e.g., from step 1420 of FIG. 14 above),and continues to step 1510, where, as described in greater detail above,a receiving transaction server 720 of the second set of transactionservers is selected (e.g., selecting a particular port of the receivingtransaction server). In step 1515, a TCP session associated with thetransaction application already in progress to the receiving transactionserver is identified, such that in step 1520, a TCP traceroute may beinitiated using ACK packets 1210, whose signature matches the TCPsession already in progress, from the receiving transaction server tothe load balancer. (Notably, as mentioned above, the TCP traceroute isECMP aware.) Accordingly, in step 1525, the sub-procedure 1500A candetermine, in reverse, a network path of layer-3 segments between thereceiving transaction server and the load balancer and one or morenetwork metrics associated with each layer-3 segment of the network pathbased on the TCP traceroute, ending the sub-procedure 1500A in step 1530(and returning to step 1435 in FIG. 14, above).

Alternatively, FIG. 15B illustrates another example sub-procedure fordeep network path analysis in accordance with one or more embodimentsdescribed herein, particularly where the network path analysis is forthe first segment of the logical transaction path (e.g., from the first(left-side) set of servers to the load balancer). The sub-procedure1500B may start at step 1535 (e.g., from step 1420 of FIG. 14 above),and continues to step 1540, where, as described in greater detail above,an originating transaction server 710 of the first set of transactionservers may be selected (e.g., selecting a particular port of theoriginating transaction server). In step 1545, a TCP traceroute may beinitiated using SYN packets 1010 associated with the transactionapplication from the originating transaction server to the load balancer(e.g., ECMP aware), in order to determine, in step 1550, a network pathof layer-3 segments between the originating transaction server and theload balancer, along with one or more network metrics associated witheach layer-3 segment of the network path of layer-3 segments between theoriginating transaction server and the load balancer based on the TCPtraceroute. The sub-procedure 1500B may then end in step 1555 (to returnto step 1435 in FIG. 14, above).

It should be noted that certain steps within procedures 1400, 1500A, and1500B may be optional as described above, and the steps shown in FIGS.14-15B are merely examples for illustration, and certain other steps maybe included or excluded as desired. Further, while a particular order ofthe steps is shown, this ordering is merely illustrative, and anysuitable arrangement of the steps may be utilized without departing fromthe scope of the embodiments herein. Moreover, while procedures 1500Aand 1500B are described separately, certain steps from each proceduremay be incorporated into each other procedure, or the procedures mayboth be performed as a full-picture scan of the logical applicationpath, and the procedures are not meant to be mutually exclusive.

The techniques described herein, therefore, provide for deep networkpath analysis for identifying network segments affecting applicationperformance. In particular, the techniques herein provide an automatedtechnique to trace the network segments that are utilized by anapplication, and to gather network metrics specific to each particularsegment. Such mechanisms are valuable for network visualization,troubleshooting, and root cause analysis, and produces fewer and morerelevant deep path analysis incidents to solve a particular degradationoccurrence. Current tools in the market rely on capturing the packets onthe network and doing a time-based correlation with the application,i.e., they are not correlated to the specific business transactions, andcannot directly correlate network degradation path information tobusiness transaction degradation path information. Other tools availablein the market involve a lot of manual stitching together of data betweenthe application and the network, thus leading to inaccuracies andincreases in the mean-time to determine the root cause of the issue.

Furthermore, the techniques herein are capable of mapping networkelements on both the left side and right side of a load balancer ineast-west network traffic. In particular, though an agent on the leftside of a load balancer can initiate synthetic traffic, the techniquesherein also describe the detection of network elements on the loadbalancer's right side (e.g., the “real IP” side), by allowing an agenton the right side to piggyback synthetic traffic on existing applicationTCP ACK traffic. Notably, the techniques herein also includes provisionsfor handling ECMP routes in east-west traffic between two applicationservices over which business transactions are exchanged (i.e., for meshnetworks using ECMP load balancing methods).

In still further embodiments of the techniques herein, a business impactof network segments affecting application performance can also bequantified. That is, because of issues related to specificapplications/processes (e.g., lost traffic, slower servers, overloadednetwork links, etc.), various corresponding business transactions mayhave been correspondingly affected for those applications/processes(e.g., online purchases were delayed, page visits were halted beforefully loading, user satisfaction or dwell time decreased, etc.), whileother processes (e.g., on other network segments or at other times)remain unaffected. The techniques herein, therefore, can correlate theisolated network segments and their network metrics with variousbusiness transactions in order to better understand the affect thenetwork issues may have had on the business transactions, accordingly.

While there have been shown and described illustrative embodiments thatprovide for identifying network segments affecting applicationperformance, it is to be understood that various other adaptations andmodifications may be made within the spirit and scope of the embodimentsherein. For example, while certain embodiments are described herein withrespect to certain types of networks in particular, the techniques arenot limited as such and may be used with any computer network,generally, in other embodiments. Moreover, while specific technologies,protocols, and associated devices have been shown, such as Java, TCP,IP, and so on, other suitable technologies, protocols, and associateddevices may be used in accordance with the techniques described above.In addition, while certain devices are shown, and with certainfunctionality being performed on certain devices, other suitable devicesand process locations may be used, accordingly. That is, the embodimentshave been shown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a server” or “by acontroller”, those skilled in the art will appreciate that agents of theapplication intelligence platform (e.g., application agents, networkagents, language agents, etc.) may be considered to be extensions of theserver (or controller) operation, and as such, any process stepperformed “by a server” need not be limited to local processing on aspecific server device, unless otherwise specifically noted as such.Furthermore, while certain aspects are described as being performed “byan agent” or by particular types of agents (e.g., application agents,network agents, etc.), the techniques may be generally applied to anysuitable software/hardware configuration (libraries, modules, etc.) aspart of an apparatus or otherwise.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in the present disclosure should not be understoodas requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method, comprising: determining, by a networkanalysis process, a trigger to initiate network path analysis for atransaction application operating over a logical transaction path havinga first segment from a first set of transaction servers to a loadbalancer and a second segment from the load balancer to a second set oftransaction servers; initiating the network path analysis, by thenetwork analysis process in response to the trigger, wherein the networkpath analysis is for the second segment of the logical transaction pathand comprises: selecting a receiving transaction server of the secondset of transaction servers; identifying a transmission control protocol(TCP) session associated with the transaction application already inprogress to the receiving transaction server; initiating a TCPtraceroute using acknowledgment (ACK) packets, whose signature matchesthe TCP session already in progress, from the receiving transactionserver to the load balancer; and determining, in reverse, a network pathof layer-3 segments between the receiving transaction server and theload balancer and one or more network metrics associated with eachlayer-3 segment of the network path based on the TCP traceroute; andidentifying, by the network analysis process, one or more specificlayer-3 segments of the network path causing performance degradation ofthe transaction application based on the network path analysis.
 2. Themethod as in claim 1, wherein the network path analysis is for the firstsegment of the logical transaction path and comprises: selecting anoriginating transaction server of the first set of transaction servers;initiating a TCP traceroute using synchronize (SYN) packets associatedwith the transaction application from the originating transaction serverto the load balancer; and determining a network path of layer-3 segmentsbetween the originating transaction server and the load balancer and oneor more network metrics associated with each layer-3 segment of thenetwork path of layer-3 segments between the originating transactionserver and the load balancer based on the TCP traceroute; and whereinthe method further comprises: identifying one or more specific layer-3segments of the network path of layer-3 segments between the originatingtransaction server and the load balancer causing performance degradationof the transaction application based on the network path analysis. 3.The method as in claim 1, wherein the trigger is the performancedegradation of the transaction application surpassing a thresholdtolerance of application-based latency.
 4. The method as in claim 1,wherein the trigger is a user request.
 5. The method as in claim 1,further comprising: determining whether the performance degradation isbased on either the first network segment or the second network segment.6. The method as in claim 5, wherein the trigger is a user request andwherein determining whether the performance degradation is based oneither the first network segment or the second network segmentcomprises: receiving an indication within the user request of whetherthe performance degradation is based on either the first network segmentor the second network segment.
 7. The method as in claim 1, wherein thefirst set of transaction servers comprise ecommerce servers and thesecond set of transaction servers comprise order servers.
 8. The methodas in claim 1, wherein the one or more network metrics are selected froma group consisting of: latency; packet drops; delay; and jitter.
 9. Themethod as in claim 1, wherein selecting the receiving transaction serverof the second set of transaction servers further comprises: selecting aparticular port of the receiving transaction server.
 10. The method asin claim 1, wherein the TCP traceroute is equal-cost multi-path (ECMP)aware.
 11. The method as in claim 1, further comprising: mitigating theperformance degradation of the transaction application based on the oneor more specific layer-3 segments causing the performance degradation.12. The method as in claim 1, further comprising: displaying the one ormore specific layer-3 segments causing the performance degradation andthe one or more network metrics associated with the one or more specificlayer-3 segments causing the performance degradation on a graphical userinterface (GUI).
 13. The method as in claim 1, wherein determining theone or more network metrics associated with each layer-3 segment of thenetwork path is based on subtracting previous total network metrics of aprevious iteration of the TCP traceroute from a current total networkmetric associated with a current iteration of the TCP traceroute.
 14. Atangible, non-transitory, computer-readable medium storing programinstructions that cause a computer to execute a process comprising:determining a trigger to initiate network path analysis for atransaction application operating over a logical transaction path havinga first segment from a first set of transaction servers to a loadbalancer and a second segment from the load balancer to a second set oftransaction servers; initiating the network path analysis, in responseto the trigger, wherein the network path analysis is for the secondsegment of the logical transaction path and comprises: selecting areceiving transaction server of the second set of transaction servers;identifying a transmission control protocol (TCP) session associatedwith the transaction application already in progress to the receivingtransaction server; initiating a TCP traceroute using acknowledgment(ACK) packets, whose signature matches the TCP session already inprogress, from the receiving transaction server to the load balancer;and determining, in reverse, a network path of layer-3 segments betweenthe receiving transaction server and the load balancer and one or morenetwork metrics associated with each layer-3 segment of the network pathbased on the TCP traceroute; and identifying one or more specificlayer-3 segments of the network path causing performance degradation ofthe transaction application based on the network path analysis.
 15. Thecomputer-readable medium as in claim 14, wherein the network pathanalysis is for the first segment of the logical transaction path andcomprises: selecting an originating transaction server of the first setof transaction servers; initiating a TCP traceroute using synchronize(SYN) packets associated with the transaction application from theoriginating transaction server to the load balancer; and determining anetwork path of layer-3 segments between the originating transactionserver and the load balancer and one or more network metrics associatedwith each layer-3 segment of the network path of layer-3 segmentsbetween the originating transaction server and the load balancer basedon the TCP traceroute; and wherein the process further comprises:identifying one or more specific layer-3 segments of the network path oflayer-3 segments between the originating transaction server and the loadbalancer causing performance degradation of the transaction applicationbased on the network path analysis.
 16. The computer-readable medium asin claim 14, wherein the one or more network metrics are selected from agroup consisting of: latency; packet drops; delay; and jitter.
 17. Thecomputer-readable medium as in claim 14, wherein the TCP traceroute isequal-cost multi-path (ECMP) aware.
 18. The computer-readable medium asin claim 14, wherein the process further comprises: mitigating theperformance degradation of the transaction application based on the oneor more specific layer-3 segments causing the performance degradation.19. The computer-readable medium as in claim 14, wherein the processfurther comprises: displaying the one or more specific layer-3 segmentscausing the performance degradation and the one or more network metricsassociated with the one or more specific layer-3 segments causing theperformance degradation on a graphical user interface (GUI).
 20. Anapparatus, comprising: one or more network interfaces to communicatewith a network; a processor coupled to the network interfaces andconfigured to execute one or more processes; and a memory configured tostore a process executable by the processor, the process when executedconfigured to: determine a trigger to initiate network path analysis fora transaction application operating over a logical transaction pathhaving a first segment from a first set of transaction servers to a loadbalancer and a second segment from the load balancer to a second set oftransaction servers; initiate the network path analysis, in response tothe trigger, wherein the network path analysis is for the second segmentof the logical transaction path and comprises: selecting a receivingtransaction server of the second set of transaction servers; identifyinga transmission control protocol (TCP) session associated with thetransaction application already in progress to the receiving transactionserver; initiating a TCP traceroute using acknowledgment (ACK) packets,whose signature matches the TCP session already in progress, from thereceiving transaction server to the load balancer; and determining, inreverse, a network path of layer-3 segments between the receivingtransaction server and the load balancer and one or more network metricsassociated with each layer-3 segment of the network path based on theTCP traceroute; and identify one or more specific layer-3 segments ofthe network path causing performance degradation of the transactionapplication based on the network path analysis.